---
title: "AI Agents"
description: "571 posts about AI Agents, summarised, each linking to the original."
---

# AI Agents
> 571 posts about AI Agents, summarised, each linking to the original.

## Articles

### [Agent platform (Part 1): How we help Grab build and run AI agents at scale](https://yomu.fyi/post/agent-platform-part-1-how-we-help-grab-build-and-run-ai-agents-at-scal.md)
- Company: [Grab](https://yomu.fyi/company/grab.md)
- Author: Kendrick Tan
- Published: Jul 24, 2026
- Type: Explainer

Grab’s AI support bot grew from an internal GPT-powered Level-0 service into the foundation for LLM-Kit, an internal framework now used by more than 500 services, alongside over 50 registered remote MCP servers and a company-wide LLM gateway handling billions of tokens monthly. The bot used a single-agent reasoning loop and a tool plane spanning Glean, Kibana, GitLab, Slack, and HTTP plugins, but scaling exposed missing evaluations, fragmented observability, provider-switching friction, and costly production plumbing. LLM-Kit addresses those problems with generated FastAPI services, LangGraph agent templates, OpenTelemetry tracing, configuration and secret-management integrations, evaluation routes, remote MCP tool discovery, and gRPC contracts, clients, health checks, and channel selection. The post concludes that framework concerns became platform concerns at 500 agents, leading to a gateway, remote MCP framework, and evals platform described as the series continues.


### [Turn And Face The Strange](https://yomu.fyi/post/turn-and-face-the-strange.md)
- Company: [Fly.io](https://yomu.fyi/company/fly-io.md)
- Published: Jul 24, 2026
- Type: Announcement

Fly.io announces a strategic shift toward Sprites, its “computers for agents,” after concluding that AI-driven software development changes the needs its public-cloud platform should serve. The company says coding agents increasingly run in cloud-hosted workstations because laptops stop when closed, while conventional cloud resources impose too much commitment for semi-disposable agent workloads. Sprites are designed to be created rapidly in large numbers, retain 100GB durable disks, use metered utility billing that pauses during inactivity, and host Internet-accessible applications. The post says a new iteration adds scaling and orchestration work plus the Sprite Block Device, while Fly Machines and platform-as-a-service features will continue. Fly.io also announces new funding and Scott Johnston as CEO, with the founder moving to an advisor role.


### [How the FDA Built an AI Platform That 85% of Its Staff Now Use Daily](https://yomu.fyi/post/how-the-fda-built-an-ai-platform-that-85-of-its-staff-now-use-daily.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Molly Just-Behr
- Published: Jul 23, 2026
- Type: Explainer

The FDA built ELSA, a generative AI platform for its 16,000 staff, on Halo, a governed Databricks data foundation created to address fragmented systems across eight centers. Those centers had separate chatbots and data stores; consolidating 50 to 60 sources enabled faster sharing, real-time streaming, and centralized access controls through Unity Catalog. Within roughly two months, ELSA adoption rose from less than 1% to 85%, while staff began building hundreds of agents weekly from standard operating procedures, regulatory guidance, and center-specific documents. MCP servers layered over Unity Catalog make governed data and tooling accessible beyond data scientists, and Databricks ML and NLP capabilities through MLflow extracted starting materials and product-supplier-manufacturer relationships from millions of submission pages. A reviewer can now request grounded starting-material information for a drug application in about three minutes instead of days, while the FDA adapts center-specific MCP tools and extends the model across its organization.


### [Permission isn't purpose: Intent-based authorization in Omnigent](https://yomu.fyi/post/permission-isn-t-purpose-intent-based-authorization-in-omnigent.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Nishith Sinha, Matei Zaharia
- Published: Jul 23, 2026
- Type: Explainer

Omnigent’s intent-based authorization addresses a gap in identity-based access control: an agent with valid credentials may follow indirect prompt injections embedded in data and perform authorized actions unrelated to the user’s task. It binds each session to a human-declared purpose and evaluates that intent before every tool call, producing permitted, consent-required, or denied verdicts. In a data-quality example, reading a customers table is allowed, dashboard publication requires approval, and an injected request to grant external access is denied despite the agent’s identity permitting that tool. The intent is fixed at design time for autonomous agents or approved by a human at session start for interactive agents, and cannot be broadened by the agent. Omnigent combines this policy with session-risk scoring and other contextual policies in a single engine where any denial wins.


### [Why A Frontier Data Agent Outperforms General Coding Agents in Quality and Cost](https://yomu.fyi/post/why-a-frontier-data-agent-outperforms-general-coding-agents-in-quality.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: The Databricks AI Research Team
- Published: Jul 23, 2026
- Type: Benchmark

An evaluation compared Genie Code with three widely used coding agents from major AI labs on 401 real internal data tasks, using a shared 20-minute wall-clock budget and independent grading. Genie Code achieved 76.6% accuracy at an estimated $0.55 per task, outperforming rivals at 55.9–72.1% accuracy and $0.91–$1.16 per task. Its advantage is attributed to semantic search across catalog and workspace assets, persistent memory of tables and business logic, and enterprise-context understanding, which reduce exploratory tool use. On discovery-heavy tasks, general-purpose agents often wandered through large workspaces or timed out on inefficient scans, while Genie Code averaged 8.3 tool calls per task. The benchmark found no accuracy-cost trade-off, although the authors note that Genie Ontology was disabled and expect it to strengthen the advantage.


### [Introducing Claude Opus 4.5](https://yomu.fyi/post/introducing-claude-opus-4-5.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 23, 2026
- Type: Announcement

Claude Opus 4.5 is available today across Claude apps, the API, and all three major cloud platforms, with the model identifier claude-opus-4-5-20251101 and pricing of $5/$25 per million tokens. The announcement positions it as a state-of-the-art model for coding, agents, computer use, deep research, spreadsheets, and slides, while reporting stronger vision, reasoning, and mathematics than predecessors. On the Claude API, an effort parameter lets developers trade off speed and spend against capability; at medium effort, Opus 4.5 matched Sonnet 4.5’s best SWE-bench Verified score using 76% fewer output tokens, while highest effort exceeded it by 4.3 percentage points using 48% fewer tokens. Additional platform changes include context compaction, advanced tool use, memory, and improved multi-agent coordination. Product updates add Claude Code’s editable Plan Mode and desktop sessions, automatic conversation summarization, and expanded Claude for Chrome and Excel access.


### [Simplify AI agent orchestration with Lakebase Postgres](https://yomu.fyi/post/simplify-ai-agent-orchestration-with-lakebase-postgres.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Li Yu, Michelle JanneyCoyle, Jon Cormack, Yarri Bryn, Alec Sorensen, Darshana Nair
- Published: Jul 22, 2026
- Type: Problem & solution

CLA and Databricks built a production document-processing application for auditing that reduces extraction time from hours to minutes without compromising quality, using Databricks-native services including Lakebase Postgres, Databricks Apps, Lakeflow Jobs, MLflow, and Unity Catalog Volumes. Lakebase serves as the orchestration layer’s single source of truth for tasks and execution attempts, coordinating long-running work, retries, leases, priorities, rate limits, costs, and status visibility. The queue uses Postgres patterns including FOR UPDATE SKIP LOCKED, priority and FIFO ordering, expiring leases for crash recovery, and database-backed concurrency controls. Databricks Jobs process PDFs through intelligent document processing and vision/LLM calls, while MLflow Tracing records execution and cost details and dashboard updates combine fast Postgres data with slower billing queries. In production, this architecture avoids external brokers and schedulers while providing durable task management, real-time visibility, and per-task cost attribution.


### [Introducing Claude Opus 4.8](https://yomu.fyi/post/introducing-claude-opus-4-8.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 22, 2026
- Type: Announcement

Anthropic introduces Claude Opus 4.8, an upgrade over Opus 4.7 for coding, agentic skills, reasoning, and practical knowledge work, with regular pricing unchanged. The release adds adjustable effort in claude.ai and Cowork, dynamic workflows in Claude Code, fast mode at 2.5× speed, and support for system entries inside the Messages API messages array. Dynamic workflows can plan work, run hundreds of parallel subagents in one session, and verify outputs; the post cites codebase-scale migrations across hundreds of thousands of lines as an example. Early evaluations and tester reports describe stronger reliability, judgment, tool efficiency, computer use, and analysis, including an approximately fourfold reduction in allowing flaws in generated code to pass unremarked.


### [Introducing Claude Sonnet 4.5](https://yomu.fyi/post/introducing-claude-sonnet-4-5.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 22, 2026
- Type: Announcement

The release introduces Claude Sonnet 4.5 as a model aimed at complex coding, agentic work, computer use, reasoning, and math, with claimed gains in alignment. It reports state-of-the-art SWE-bench Verified performance and a 61.4% OSWorld score, up from Sonnet 4’s 42.2%, while noting more than 30 hours of observed focus on complex multi-step tasks. Alongside the model, the company released Claude Agent SDK, added Claude Code checkpoints, a refreshed terminal interface, native VS Code extension, context editing, and a memory tool, and brought code execution and file creation to paid Claude apps. Sonnet 4.5 is available through the Claude API as claude-sonnet-4-5 at $3/$15 per million tokens, the same pricing as Sonnet 4. It is released under AI Safety Level 3 protections, with reported reductions in concerning behaviors and improved defenses against prompt injection, while the temporary Imagine with Claude preview is available to Max subscribers for five days.


### [Claude Opus 4.6](https://yomu.fyi/post/claude-opus-4-6.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 22, 2026
- Type: Announcement

Claude Opus 4.6 improves on Opus 4.5 with stronger coding, planning, debugging, code review, long-running agentic work, and performance in large codebases. It is the first Opus-class model with a beta 1M-token context window, while API features include adaptive thinking, four effort levels, context compaction, and up to 128k output tokens. In reported evaluations, it achieves the highest score on Terminal-Bench 2.0, leads frontier models on Humanity’s Last Exam and BrowseComp, and exceeds GPT-5.2 by around 144 Elo points and Opus 4.5 by 190 points on GDPval-AA. The release also adds Claude Code agent teams, upgraded Excel capabilities, a PowerPoint research preview, and availability on claude.ai, the API, and major cloud platforms, with base pricing of $5/$25 per million tokens.


### [Introducing Sonnet 4.6](https://yomu.fyi/post/introducing-sonnet-4-6.md)
- Company: [Anthropic](https://yomu.fyi/company/anthropic.md)
- Published: Jul 21, 2026
- Type: Announcement

Claude Sonnet 4.6 is introduced as an upgrade across coding, computer use, long-context reasoning, agent planning, knowledge work, and design. It adds a 1M token context window in beta, becomes the default for Free and Pro users in claude.ai and Claude Cowork, and keeps Sonnet 4.5’s starting price of $3/$15 per million tokens. Early testing reports that users preferred it over Sonnet 4.5 in Claude Code roughly 70% of the time and over Opus 4.5 59% of the time. The release also reports gains on OSWorld computer use, prompt-injection resistance, OfficeQA document comprehension, and long-horizon planning, while adding adaptive and extended thinking, beta context compaction, and generally available code execution, memory, programmatic tool calling, tool search, and tool-use examples.


### [The last mile: why great first-party data still doesn't make great marketing](https://yomu.fyi/post/the-last-mile-why-great-first-party-data-still-doesn-t-make-great-mark.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Michael Burton, Katy Yuan
- Published: Jul 21, 2026
- Type: Problem & solution

Modern data platforms and sophisticated marketing systems can still fail to produce timely customer experiences when no bridge connects first-party data to campaign activation. The post explains Scott Brinker’s “composable canvas,” a five-ring architecture centered on a unified data core, with semantic layer, CaaS, decisioning, and apps and agents operating on shared data without repeated movement. In contrast, batch files, dashboards, and disconnected teams can leave autonomous agents unable to trigger campaigns or propensity models unused. It recommends closing this last mile through activation-ready data architecture, self-service marketing analytics, and one narrowly scoped AI agent tied to a measurable campaign outcome. Examples in the post include a 4X model conversion rate, a four-week account scoring launch, and grocery offer flows that changed weekly preparation from hours to minutes.


### [Announcing the Public Preview of Discover and Domains, powered by Unity Catalog](https://yomu.fyi/post/announcing-the-public-preview-of-discover-and-domains-powered-by-unity.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Stef Bran Melendez, Kelly Albano
- Published: Jul 21, 2026
- Type: Announcement

Databricks announces the Public Preview of Domains and the Discover page, powered by Unity Catalog, to help people and agents identify relevant, high-quality, and safe data and AI assets. Domains organize tables, dashboards, notebooks, queries, metric views, Genie Agents, and apps by business structure, while Discover provides an internal marketplace with search, certification signals, popularity and trending indicators, and AI-powered recommendations. Data stewards can create domains and subdomains, certify assets, add descriptions and contacts, and curate page sections and pinned content. Domains extend Unity Catalog Semantics and feed Genie Ontology, giving agents business context for narrowing retrieval, prioritizing trusted assets, and interpreting metrics within each user’s existing permissions. The features are available in Public Preview through a Databricks workspace, where organizations can create domains and curate assets.


### [Devin Outposts on Modal](https://yomu.fyi/post/devin-outposts-on-modal.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Jul 21, 2026
- Type: Announcement

Devin, an AI software engineer by Cognition, plans, writes, tests, and ships code semi-autonomously. Cognition now extends Devin with Outposts, moving execution into user-controlled environments while reasoning stays in Cognition’s cloud. Modal is a launch partner with modal-devin, an open-source integration running Outpost sessions in Modal Sandboxes with pre-built custom images, fast cold-starts, snapshotting, and cost-efficient bursting. For example, a task that requires a GPU fails immediately in the default sandbox but succeeds on a Modal-backed Outpost that spins up a GPU on demand and scales to zero when done. One command, uvx modal-devin init, sets up secure infrastructure with a scheduler and worker functions, and each team can have its own outpost with separate credentials.


### [Scaling Agentic RL: High-Throughput Agentic Training with Tunix](https://yomu.fyi/post/scaling-agentic-rl-high-throughput-agentic-training-with-tunix.md)
- Company: [Google](https://yomu.fyi/company/google.md)
- Author: Haoyu Gao, Lance Wang, Shadi Noghabi, Tianshu Bao, Weiren Yu
- Published: Jul 21, 2026
- Type: Explainer

Tunix is presented as a post-training library for scaling agentic reinforcement learning, where multi-turn tool use and environment latency can leave TPUs idle. Its design combines an asyncio-based, high-concurrency trajectory collector with asynchronous vLLM-TPU and SGLang-Jax integration, then streams variable-length trajectories through a producer-consumer queue rather than waiting for a complete synchronous batch. Composable agent and environment abstractions expose APIs for custom tools, benchmarks, and external systems while preserving multi-turn token handling and lifecycle management. Lightweight RL-specific metrics correlate rollout, training, weight synchronization, model-call, and environment stages with TPU timelines, complementing targeted XProf traces. The stated outcome is a more observable pipeline intended to reduce stalls, keep accelerators utilized, and support agentic RL natively in the JAX/TPU ecosystem.


### [The three ways AI unlocks transformation in Retail, Travel, and Consumer Goods](https://yomu.fyi/post/the-three-ways-ai-unlocks-transformation-in-retail-travel-and-consumer.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Rob Saker
- Published: Jul 20, 2026
- Type: Explainer

The piece argues that retail, travel, and consumer-goods companies face one problem in three forms: signals are not trusted, arrive too late, or cost too much to process at scale. It contrasts business intelligence, which depends on structured schemas, predefined questions, and dashboards, with AI systems that read unstructured data, reason probabilistically across signals, and connect decisions to action. Examples include reported improvements from Harmons’ shelf scanning, faster consumer-insight work at a health and hygiene company, and travel applications spanning maintenance, pricing, and concierge services. Its proposed architecture combines broad ingestion and open storage with governance, evaluation, model and agent controls, and applications that operate on a substrate, presenting current, coherent data as the foundation for organizations that can act.


### [Agent swarms and the new model economics](https://yomu.fyi/post/agent-swarms-and-the-new-model-economics.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Wilson Lin
- Published: Jul 20, 2026
- Type: Benchmark

Agent swarms are evaluated as a way to scale software work beyond what a single long-running agent can reliably manage. The test revisits building SQLite in Rust from its documentation, comparing old and new swarms with identical models and time budgets against a held-out SQL suite; the new system performed better in every configuration, reaching 80% with Grok 4.5 in four hours. Planners decompose task trees while workers execute narrower pieces, supported by a purpose-built version control system and mechanisms for design conflicts, merge collisions, megafiles, intentional breakage, and review. Reported results include a full-suite pass using 9,908 engine-code lines versus 64,305 for the old harness, while costs ranged from $1,339 for an Opus 4.8 hybrid to $10,565 for GPT-5.5 alone, indicating that cheaper models can perform most execution after frontier planning resolves ambiguity.


### [AI Agent Orchestration: A Guide for Enterprise Systems](https://yomu.fyi/post/ai-agent-orchestration-a-guide-for-enterprise-systems.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Jul 20, 2026
- Type: Explainer

AI agent orchestration coordinates multiple specialized agents by managing task assignment, shared state, communication, and execution sequencing across complex enterprise workflows. The guide contrasts autonomous agents, which reason about subtasks and adapt to intermediate results, with fixed workflows, and recommends decomposing processes into single-responsibility agents with documented input/output contracts and least-privilege access. It describes centralized, decentralized, hierarchical, hybrid, federated, and emergent patterns, noting that pattern selection trades control, resilience, scalability, and auditability according to risk. Implementation guidance includes assigning human owners, defining accuracy, latency, and escalation targets, establishing failure paths, instrumenting a minimal prototype, and measuring a pilot against documented baseline processes. Organizations using multi-agent systems report 35% faster task completion and a 30% efficiency increase with specialized agents, while high-risk or irreversible actions still require human approval gates and audit trails.


### [Tech builds on AI. Finance protects the margin.](https://yomu.fyi/post/tech-builds-on-ai-finance-protects-the-margin.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Madelyn Mullen
- Published: Jul 17, 2026
- Type: Explainer

AI-native tech companies must protect unit economics as agents accelerate changes in compute consumption, pricing, and revenue recognition, while gross margins remain below classic software levels. Finance teams built around extracts, spreadsheets, and monthly reconciliation can miss repricing changes, metering errors, and compute-commitment risk. The proposed foundation is an evolving ontology that keeps product, plan, usage, and cost meanings current, with Stripe data entering Unity Catalog through OpenSharing and Lakebase providing transactional Postgres on the lakehouse. Genie One uses that ontology to answer governed, sourced questions about gross margin, consumption revenue at risk, and compute spend, while people retain decision authority. The post describes organizations using Databricks to consolidate reporting, forecasting, workflows, and finance applications, positioning a shared data-and-AI platform as the path from an initial answer to an ongoing finance platform.


### [Building a soccer coaching app on Databricks](https://yomu.fyi/post/building-a-soccer-coaching-app-on-databricks.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Samwel Emmanuel, Sheridan Harris, Andrew Helmreich, Kush Patel, Nick Ragonese
- Published: Jul 17, 2026
- Type: Problem & solution

Coach’s Corner, also called La Pizarra, turns high-frequency soccer tracking data into a bench-side application for replay, tactical analysis, scouting, standings, and agent-generated dossiers. Built as a Databricks App, it ingests NDJSON feeds at 25 frames per second through Auto Loader and Spark Declarative Pipelines, enforcing 46 data-quality expectations across bronze, silver, and gold layers. Liquid clustering supports 1–3-second DBSQL queries, while Lakebase synchronizes gold data to Postgres for millisecond replay reads and separates sequential playback from exploratory analytics. The scouting layer grounds Genie, Vector Search, a Unity Catalog-registered xG model, and an Agent Bricks supervisor in governed data, with Claude calls routed through the Unity AI Gateway, MLflow tracing, and a deterministic fallback. Together, these components are presented as a way to deliver traceable insights within seconds without forcing coaches to interpret raw tables or analysts to relay every result.


[Newer posts](https://yomu.fyi/topic/ai-agents/page/14.md) · [Older posts](https://yomu.fyi/topic/ai-agents/page/16.md)